We examine the effectiveness of TransBoost by using a variety of pretrained neural networks, datasets, and baseline models. Our extensive study examines a spectrum of ResNet architectures and a variety of modern architectures such as ConvNext [1]}, MobileNetv3 [2]} and ViT [3]}.
Figure REF  highlights
some of TransBoost's results on the ImageNet dataset, which demonstrate impressive and consistent accuracy gains, leading to state-of-the-art performance compared to standard (inductive) classification as well as versus SSL and t-FSL methods (see full results in Section REF ).
